class_docstr: BatchNorm2d where the batch statistics and the affine parameters are fixed.
  
  It contains non-trainable buffers called
  "weight" and "bias", "running_mean", "running_var",
  initialized to perform identity transformation.
  
  The pre-trained backbone models from Caffe2 only contain "weight" and "bias",
  which are computed from the original four parameters of BN.
  The affine transform `x * weight + bias` will perform the equivalent
  computation of `(x - running_mean) / sqrt(running_var) * weight + bias`.
  When loading a backbone model from Caffe2, "running_mean" and "running_var"
  will be left unchanged as identity transformation.
  
  Other pre-trained backbone models may contain all 4 parameters.
  
  The forward is implemented by `F.batch_norm(..., training=False)`.
code: def __repr__(self):
      return "FrozenBatchNorm2d(num_features={}, eps={})".format(
          self.num_features, self.eps
      )
  
imports: import torch
  import torch.distributed
  from fvcore.nn.distributed import differentiable_all_reduce
  from torch import nn
  from torch.nn import functional
  from detectron2.utils import comm, env
  from .wrappers import BatchNorm2d
label_desc: Use image node_id 4 for calling the FrozenBatchNorm2d obj's underlying member method code with example usage: obj.__repr__() and returns: str